Generally, “insurance” is an agreement by which an insurer, sometimes referred to as an underwriter, undertakes to indemnify the insured party against loss, damage, or liability arising from certain risks, in exchange for consideration. The consideration paid by an insured party is typically referred to as a “premium,” which is paid to keep the insurance in effect. An “insurance policy” is a contract of insurance that defines the rights and duties of the contracting parties. A typical insurance policy includes limits on the amount of risk that the insurer will cover.
For the purposes of the following discussion, an “insurance product” includes more than the insurance policy. It also includes services, distribution channels, and other components that may impact the customer experience.
Property insurance protects persons or businesses from financial loss caused by perils. Perils can include losses due to fire, water, earthquake, wind, explosions, aircraft damage (as when an aircraft crashes into a structure), lightning, hail, riot or civil commotion, smoke, vandalism, falling objects, theft, volcanic eruptions, and freezing. An insurance policy providing property insurance may cover some or all of these categories of perils. By paying a premium on a regular basis, a policyholder is insured against a loss caused by a peril within the scope of the policy.
Insurance rates are determined through an actuarial process. The process looks at data related to customer characteristics to determine differences in expected loss costs for different customers. One part of the actuarial process, referred to as “territorial ratemaking,” is an actuarial process for adjusting rates used in insurance or other risk transfer mechanisms based on location. The ratemaking process is prospective because property and casualty rates are typically developed prior to the transfer of risk from a customer to an insurance company. Since insurance policy rates reflect an estimate of the expected value of future costs, estimates of future losses are used when determining insurance rates.
By analyzing loss-cost data of a region over a number of years, a company can estimate future exposure to risk more accurately by invoking mathematical methodologies. In the insurance industry, a common practice for determining rates involves estimating future costs by looking at past loss-cost data. Different actuarial methodologies have been developed to further improve the credibility of the data available in the ratemaking process.
The credibility of the data is considered the predictive value that an actuary attaches to a particular body of data. One way of increasing the credibility of the data is to increase the size of the group or to make groupings more homogeneous. Loss-cost data is frequently broken down into regions defined by postal ZIP codes as a convenient subset of a county or state. In fact, actuaries often use information from the government in ratemaking. Some ZIP codes, however, may have little or no recent data with which to predict expected loss costs and thus determine appropriate future premiums.
A “loss-cost property-estimating methodology” identifies, organizes, and stratifies five-year historical loss costs by ZIP code and uses geographical spatial-smoothing techniques. Each ZIP code is assigned a new loss-cost value, and the new smoothed loss-cost data by ZIP code can be further processed to form rating zones.
Although many actuarial methods have been developed to improve the credibility of data in the ratemaking process, the absence of data in certain regions may lower the data's predictive value. By increasing the predictive value of the data only slightly, an insurance company may gain a significant advantage over its competition.
The values of pure premium data can vary significantly between ZIP codes and from year to year. In addition, losses from a given peril are sometimes just as likely to occur near the actual location as at that location. The loss may have occurred near a ZIP-code boundary, however, and the adjacent ZIP code may have had few if any such losses during the period being measured. Consequently, it is useful to incorporate the experience of neighboring properties while recognizing that the relevance of the neighboring experience will diminish with distance.
Different methodologies have been used to smooth data. “Spatial filtering” describes the methods used to compute spatial density estimates for events that have been observed at various geographically-defined locations. Spatial filters rely on nearby adjacent values to estimate a value at a given point. The filters reduce the variability in a data set while retaining the local features of the data. By applying this technique to pure premiums, one can identify areas that have larger or smaller values then average.
In a geospatial method, pure premium data is compared and mathematically adjusted with respect to neighboring regions such as ZIP codes. This is accomplished by comparing the pure premium of a given ZIP code to all the other ZIP codes in the state. The loss cost data is weighted to reflect its geographical relationship or the distance of the target ZIP code from the individual ZIP codes in the state or targeted region.
In a geospatial smoothing method, a line is computed from the geographic centroid of a target ZIP code to the geographic centroid of each individual ZIP code in the state. Then, the pure premiums are weighted based on their relative distance from the subject ZIP code.